
#importing libraries
import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")


df = pd.read_csv('creditcard.csv')

df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    284807 non-null  float64
 1   V1      284807 non-null  float64
 2   V2      284807 non-null  float64
 3   V3      284807 non-null  float64
 4   V4      284807 non-null  float64
 5   V5      284807 non-null  float64
 6   V6      284807 non-null  float64
 7   V7      284807 non-null  float64
 8   V8      284807 non-null  float64
 9   V9      284807 non-null  float64
 10  V10     284807 non-null  float64
 11  V11     284807 non-null  float64
 12  V12     284807 non-null  float64
 13  V13     284807 non-null  float64
 14  V14     284807 non-null  float64
 15  V15     284807 non-null  float64
 16  V16     284807 non-null  float64
 17  V17     284807 non-null  float64
 18  V18     284807 non-null  float64
 19  V19     284807 non-null  float64
 20  V20     284807 non-null  float64
 21  V21     284807 non-null  float64
 22  V22     284807 non-null  float64
 23  V23     284807 non-null  float64
 24  V24     284807 non-null  float64
 25  V25     284807 non-null  float64
 26  V26     284807 non-null  float64
 27  V27     284807 non-null  float64
 28  V28     284807 non-null  float64
 29  Amount  284807 non-null  float64
 30  Class   284807 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 67.4 MB


df.describe()


df.describe().T


# High Dimensional data with different scales


#Dropping Null rows
df.dropna(inplace = True)


# Dropping Duplicated Row
dup = df.duplicated().sum()
print(dup)
df.drop_duplicates(inplace=True)

1081


df.isnull().sum()

Time      0
V1        0
V2        0
V3        0
V4        0
V5        0
V6        0
V7        0
V8        0
V9        0
V10       0
V11       0
V12       0
V13       0
V14       0
V15       0
V16       0
V17       0
V18       0
V19       0
V20       0
V21       0
V22       0
V23       0
V24       0
V25       0
V26       0
V27       0
V28       0
Amount    0
Class     0
dtype: int64


# Checking Null entries
df.isna().any(axis=1).sum()

0


# subsetting columns for correlation 
col_selected = list(df.columns)


col_selected

['Time',
 'V1',
 'V2',
 'V3',
 'V4',
 'V5',
 'V6',
 'V7',
 'V8',
 'V9',
 'V10',
 'V11',
 'V12',
 'V13',
 'V14',
 'V15',
 'V16',
 'V17',
 'V18',
 'V19',
 'V20',
 'V21',
 'V22',
 'V23',
 'V24',
 'V25',
 'V26',
 'V27',
 'V28',
 'Amount',
 'Class']


col_selected = [x for x in col_selected if x not in ['Time','Amount']]


col_selected

['V1',
 'V2',
 'V3',
 'V4',
 'V5',
 'V6',
 'V7',
 'V8',
 'V9',
 'V10',
 'V11',
 'V12',
 'V13',
 'V14',
 'V15',
 'V16',
 'V17',
 'V18',
 'V19',
 'V20',
 'V21',
 'V22',
 'V23',
 'V24',
 'V25',
 'V26',
 'V27',
 'V28',
 'Class']


df_selected = df[col_selected]


df_selected


# Correlation between numerical columns
plt.figure(figsize=(20,20))
sns.heatmap(df_selected.corr(),annot=True,cmap='coolwarm', fmt='.2f')

<Axes: >


# No clear idea in important features for modelling, so there is requirement to proceed with diffrent feature selection techniques.!


# Feature Selection


# Method 1: Fisher Score (chi-square implementation)


from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.preprocessing import MinMaxScaler

X = df_selected.iloc[:, :-1]  # Features
y = df_selected.iloc[:, -1]   # Labels

scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

# Calculate chi-square scores on the scaled features
chi2_scores = chi2(X_scaled, y)

# Extract p-values from the chi2_scores
p_values = chi2_scores[1]

# Calculate Fisher Score
fisher_scores = -np.log10(p_values)

k = 10  
selector = SelectKBest(score_func=lambda X, y: fisher_scores, k=k)
X_new = selector.fit_transform(X_scaled, y)

# Print the selected features
chi2_features = X.columns[selector.get_support()]
print("Selected Features:", chi2_features)

Selected Features: Index(['V3', 'V4', 'V9', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18'], dtype='object')


# Method 2: ANOVA F-value For Feature Selection


from sklearn.feature_selection import f_classif
# Select the top k features based on ANOVA F-value
k = 10
selector = SelectKBest(score_func=f_classif, k=k)
X_new = selector.fit_transform(X, y)

# Print the selected features
anova_features = X.columns[selector.get_support()]
print("Selected Features:", anova_features)

Selected Features: Index(['V3', 'V4', 'V7', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18'], dtype='object')


# Creating Ensemble of Important Features
# Creating Sets from chi2_features and anova_features
a = set(chi2_features)
b = set(anova_features)
Top_Features = sorted(list(a.union(b)), reverse=True)
Top_Features

['V9', 'V7', 'V4', 'V3', 'V18', 'V17', 'V16', 'V14', 'V12', 'V11', 'V10']


len(Top_Features)

11


#Total 11 most significant features are selected for the further studies


# Creating a new frame
newCol = Top_Features
newCol

['V9', 'V7', 'V4', 'V3', 'V18', 'V17', 'V16', 'V14', 'V12', 'V11', 'V10']


newCol.append("Class")


newCol

['V9',
 'V7',
 'V4',
 'V3',
 'V18',
 'V17',
 'V16',
 'V14',
 'V12',
 'V11',
 'V10',
 'Class']


dfFinal = df[newCol]


dfFinal


# Reordering
dfOrdered = df.reindex(columns=['V3','V4','V7','V9','V10','V11','V12','V14','V16','V17','V18','Class'])


dfOrdered


# Checing Balance in the Data
NonFraud = dfOrdered[dfOrdered['Class'] == 0]
Fraud = dfOrdered[dfOrdered['Class'] == 1]


len(NonFraud)

283253


Fraud

473


Pecentage_Fraud = len(Fraud)/len(dfOrdered)*100


Pecentage_Fraud

0.1667101358352777


# 0.16% data belongs to fraud and 99.833% Non-Fraud.
100 - Pecentage_Fraud

99.83328986416473


# Undersampling must be done on majority samples of the target variable.


# Undersampling through unbiased random row selection
n = int(round(len(Fraud)*1.3,0))

n

615


undersampledDF_NonFraud = NonFraud.sample(n)


undersampledDF_NonFraud


Fraud


balancedDF = pd.concat([undersampledDF_NonFraud, Fraud], axis=0, ignore_index=True)


balancedDF


# Correlation between numerical columns
plt.figure(figsize=(20,20))
sns.heatmap(balancedDF.corr(),annot=True,cmap='coolwarm', fmt='.2f')

<Axes: >


# Checking outliers


# Create a 2x6 subplot grid for vertical boxplots using Seaborn
plt.figure(figsize=(16, 6))
for i, column in enumerate(balancedDF.columns):
    plt.subplot(2, 6, i + 1)
    sns.boxplot(data=balancedDF[column])
    plt.title(column)
plt.tight_layout()
plt.show()


# Since values are from PCA, no need to treat the outliers, if we treat then we tend to loose the information


sns.pairplot(balancedDF, diag_kind='kde', hue='Class')

<seaborn.axisgrid.PairGrid at 0x2373ce3b970>


# Feature Engineering


from sklearn.preprocessing import StandardScaler


X = balancedDF.iloc[:, :-1]  # Features
y = balancedDF.iloc[:, -1]   # Labels

X


scaler = StandardScaler()
X_Std = scaler.fit_transform(X)


X_Std

array([[ 0.39234431, -0.68309682,  0.56159126, ...,  0.58679063,
         0.51129711,  0.83224533],
       [-0.06880616, -0.18677244,  0.18519924, ...,  0.69312608,
         0.75506609,  0.82104779],
       [ 0.6650088 , -0.48975088,  0.30921861, ...,  0.59424842,
         0.45247238,  0.20348263],
       ...,
       [ 0.12001645, -0.46136845,  0.01031297, ...,  0.03779413,
        -0.40336555,  0.17886226],
       [-0.43327762, -0.03083895,  0.01540975, ..., -0.25041902,
        -0.32892231, -0.24631296],
       [ 0.05580628, -0.48040492,  0.47882646, ...,  0.68410027,
         0.64775379,  0.92503046]])


columns = X.columns
X_Final = pd.DataFrame(data=X_Std, columns=columns)


X_Final

y

0       0
1       0
2       0
3       0
4       0
       ..
1083    1
1084    1
1085    1
1086    1
1087    1
Name: Class, Length: 1088, dtype: int64


# Splitting Balanced and Standardised Data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_Final,y,test_size=0.3,random_state=42)


X_train


X_test


y_train

1034    1
249     0
620     1
837     1
71      0
       ..
330     0
466     0
121     0
1044    1
860     1
Name: Class, Length: 761, dtype: int64


y_test

355    0
56     0
478    0
701    1
724    1
      ..
877    1
585    0
305    0
716    1
631    1
Name: Class, Length: 327, dtype: int64


# Building ML Model to predict Credit card Frauds


from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score, classification_report
import joblib


models = {
    'Logistic Regression': LogisticRegression(),
    'Decision Tree': DecisionTreeClassifier(),
    'SVM': SVC(),
    'Naive Bayes': GaussianNB(),
    'K-Nearest Neighbors': KNeighborsClassifier(),
    'Random Forest': RandomForestClassifier(),
    'Gradient Boosting': GradientBoostingClassifier()
}

Model_Outcomes = {}
for model_name, model in models.items():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    report = classification_report(y_test, y_pred)
    Model_Outcomes[model_name] = f'{round(accuracy,2)}%'
    joblib.dump(model_name, f'{model_name}.pkl')
    print(f"Model: {model_name}")
    print(f"Accuracy: {accuracy:.2f}")
    print("Classification Report:\n", report)
Model_Outcomes

Model: Logistic Regression
Accuracy: 0.95
Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.98      0.96       191
           1       0.97      0.90      0.94       136

    accuracy                           0.95       327
   macro avg       0.95      0.94      0.95       327
weighted avg       0.95      0.95      0.95       327

Model: Decision Tree
Accuracy: 0.92
Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.93      0.93       191
           1       0.90      0.90      0.90       136

    accuracy                           0.92       327
   macro avg       0.92      0.91      0.91       327
weighted avg       0.92      0.92      0.92       327

Model: SVM
Accuracy: 0.95
Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.99      0.96       191
           1       0.99      0.90      0.94       136

    accuracy                           0.95       327
   macro avg       0.96      0.95      0.95       327
weighted avg       0.96      0.95      0.95       327

Model: Naive Bayes
Accuracy: 0.94
Classification Report:
               precision    recall  f1-score   support

           0       0.91      1.00      0.95       191
           1       1.00      0.87      0.93       136

    accuracy                           0.94       327
   macro avg       0.96      0.93      0.94       327
weighted avg       0.95      0.94      0.94       327

Model: K-Nearest Neighbors
Accuracy: 0.95
Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.98      0.96       191
           1       0.97      0.92      0.94       136

    accuracy                           0.95       327
   macro avg       0.96      0.95      0.95       327
weighted avg       0.95      0.95      0.95       327

Model: Random Forest
Accuracy: 0.96
Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.99      0.96       191
           1       0.98      0.91      0.95       136

    accuracy                           0.96       327
   macro avg       0.96      0.95      0.96       327
weighted avg       0.96      0.96      0.96       327

Model: Gradient Boosting
Accuracy: 0.94
Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.95      0.95       191
           1       0.93      0.92      0.92       136

    accuracy                           0.94       327
   macro avg       0.93      0.93      0.93       327
weighted avg       0.94      0.94      0.94       327

{'Logistic Regression': '0.95%',
 'Decision Tree': '0.92%',
 'SVM': '0.95%',
 'Naive Bayes': '0.94%',
 'K-Nearest Neighbors': '0.95%',
 'Random Forest': '0.96%',
 'Gradient Boosting': '0.94%'}


# Random Forest Offering Most Promising Result


# END OF MODELLING


# Assignment Submitted by VINAY M on 15-09-2023

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	...	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62	0
1	0.0	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	...	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	2.69	0
2	1.0	-1.358354	-1.340163	1.773209	0.379780	-0.503198	1.800499	0.791461	0.247676	-1.514654	...	0.247998	0.771679	0.909412	-0.689281	-0.327642	-0.139097	-0.055353	-0.059752	378.66	0
3	1.0	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	...	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	123.50	0
4	2.0	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	...	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	69.99	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
284802	172786.0	-11.881118	10.071785	-9.834783	-2.066656	-5.364473	-2.606837	-4.918215	7.305334	1.914428	...	0.213454	0.111864	1.014480	-0.509348	1.436807	0.250034	0.943651	0.823731	0.77	0
284803	172787.0	-0.732789	-0.055080	2.035030	-0.738589	0.868229	1.058415	0.024330	0.294869	0.584800	...	0.214205	0.924384	0.012463	-1.016226	-0.606624	-0.395255	0.068472	-0.053527	24.79	0
284804	172788.0	1.919565	-0.301254	-3.249640	-0.557828	2.630515	3.031260	-0.296827	0.708417	0.432454	...	0.232045	0.578229	-0.037501	0.640134	0.265745	-0.087371	0.004455	-0.026561	67.88	0
284805	172788.0	-0.240440	0.530483	0.702510	0.689799	-0.377961	0.623708	-0.686180	0.679145	0.392087	...	0.265245	0.800049	-0.163298	0.123205	-0.569159	0.546668	0.108821	0.104533	10.00	0
284806	172792.0	-0.533413	-0.189733	0.703337	-0.506271	-0.012546	-0.649617	1.577006	-0.414650	0.486180	...	0.261057	0.643078	0.376777	0.008797	-0.473649	-0.818267	-0.002415	0.013649	217.00	0

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	284807.000000	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	...	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	284807.000000	284807.000000
mean	94813.859575	1.168375e-15	3.416908e-16	-1.379537e-15	2.074095e-15	9.604066e-16	1.487313e-15	-5.556467e-16	1.213481e-16	-2.406331e-15	...	1.654067e-16	-3.568593e-16	2.578648e-16	4.473266e-15	5.340915e-16	1.683437e-15	-3.660091e-16	-1.227390e-16	88.349619	0.001727
std	47488.145955	1.958696e+00	1.651309e+00	1.516255e+00	1.415869e+00	1.380247e+00	1.332271e+00	1.237094e+00	1.194353e+00	1.098632e+00	...	7.345240e-01	7.257016e-01	6.244603e-01	6.056471e-01	5.212781e-01	4.822270e-01	4.036325e-01	3.300833e-01	250.120109	0.041527
min	0.000000	-5.640751e+01	-7.271573e+01	-4.832559e+01	-5.683171e+00	-1.137433e+02	-2.616051e+01	-4.355724e+01	-7.321672e+01	-1.343407e+01	...	-3.483038e+01	-1.093314e+01	-4.480774e+01	-2.836627e+00	-1.029540e+01	-2.604551e+00	-2.256568e+01	-1.543008e+01	0.000000	0.000000
25%	54201.500000	-9.203734e-01	-5.985499e-01	-8.903648e-01	-8.486401e-01	-6.915971e-01	-7.682956e-01	-5.540759e-01	-2.086297e-01	-6.430976e-01	...	-2.283949e-01	-5.423504e-01	-1.618463e-01	-3.545861e-01	-3.171451e-01	-3.269839e-01	-7.083953e-02	-5.295979e-02	5.600000	0.000000
50%	84692.000000	1.810880e-02	6.548556e-02	1.798463e-01	-1.984653e-02	-5.433583e-02	-2.741871e-01	4.010308e-02	2.235804e-02	-5.142873e-02	...	-2.945017e-02	6.781943e-03	-1.119293e-02	4.097606e-02	1.659350e-02	-5.213911e-02	1.342146e-03	1.124383e-02	22.000000	0.000000
75%	139320.500000	1.315642e+00	8.037239e-01	1.027196e+00	7.433413e-01	6.119264e-01	3.985649e-01	5.704361e-01	3.273459e-01	5.971390e-01	...	1.863772e-01	5.285536e-01	1.476421e-01	4.395266e-01	3.507156e-01	2.409522e-01	9.104512e-02	7.827995e-02	77.165000	0.000000
max	172792.000000	2.454930e+00	2.205773e+01	9.382558e+00	1.687534e+01	3.480167e+01	7.330163e+01	1.205895e+02	2.000721e+01	1.559499e+01	...	2.720284e+01	1.050309e+01	2.252841e+01	4.584549e+00	7.519589e+00	3.517346e+00	3.161220e+01	3.384781e+01	25691.160000	1.000000

	count	mean	std	min	25%	50%	75%	max
Time	284807.0	9.481386e+04	47488.145955	0.000000	54201.500000	84692.000000	139320.500000	172792.000000
V1	284807.0	1.168375e-15	1.958696	-56.407510	-0.920373	0.018109	1.315642	2.454930
V2	284807.0	3.416908e-16	1.651309	-72.715728	-0.598550	0.065486	0.803724	22.057729
V3	284807.0	-1.379537e-15	1.516255	-48.325589	-0.890365	0.179846	1.027196	9.382558
V4	284807.0	2.074095e-15	1.415869	-5.683171	-0.848640	-0.019847	0.743341	16.875344
V5	284807.0	9.604066e-16	1.380247	-113.743307	-0.691597	-0.054336	0.611926	34.801666
V6	284807.0	1.487313e-15	1.332271	-26.160506	-0.768296	-0.274187	0.398565	73.301626
V7	284807.0	-5.556467e-16	1.237094	-43.557242	-0.554076	0.040103	0.570436	120.589494
V8	284807.0	1.213481e-16	1.194353	-73.216718	-0.208630	0.022358	0.327346	20.007208
V9	284807.0	-2.406331e-15	1.098632	-13.434066	-0.643098	-0.051429	0.597139	15.594995
V10	284807.0	2.239053e-15	1.088850	-24.588262	-0.535426	-0.092917	0.453923	23.745136
V11	284807.0	1.673327e-15	1.020713	-4.797473	-0.762494	-0.032757	0.739593	12.018913
V12	284807.0	-1.247012e-15	0.999201	-18.683715	-0.405571	0.140033	0.618238	7.848392
V13	284807.0	8.190001e-16	0.995274	-5.791881	-0.648539	-0.013568	0.662505	7.126883
V14	284807.0	1.207294e-15	0.958596	-19.214325	-0.425574	0.050601	0.493150	10.526766
V15	284807.0	4.887456e-15	0.915316	-4.498945	-0.582884	0.048072	0.648821	8.877742
V16	284807.0	1.437716e-15	0.876253	-14.129855	-0.468037	0.066413	0.523296	17.315112
V17	284807.0	-3.772171e-16	0.849337	-25.162799	-0.483748	-0.065676	0.399675	9.253526
V18	284807.0	9.564149e-16	0.838176	-9.498746	-0.498850	-0.003636	0.500807	5.041069
V19	284807.0	1.039917e-15	0.814041	-7.213527	-0.456299	0.003735	0.458949	5.591971
V20	284807.0	6.406204e-16	0.770925	-54.497720	-0.211721	-0.062481	0.133041	39.420904
V21	284807.0	1.654067e-16	0.734524	-34.830382	-0.228395	-0.029450	0.186377	27.202839
V22	284807.0	-3.568593e-16	0.725702	-10.933144	-0.542350	0.006782	0.528554	10.503090
V23	284807.0	2.578648e-16	0.624460	-44.807735	-0.161846	-0.011193	0.147642	22.528412
V24	284807.0	4.473266e-15	0.605647	-2.836627	-0.354586	0.040976	0.439527	4.584549
V25	284807.0	5.340915e-16	0.521278	-10.295397	-0.317145	0.016594	0.350716	7.519589
V26	284807.0	1.683437e-15	0.482227	-2.604551	-0.326984	-0.052139	0.240952	3.517346
V27	284807.0	-3.660091e-16	0.403632	-22.565679	-0.070840	0.001342	0.091045	31.612198
V28	284807.0	-1.227390e-16	0.330083	-15.430084	-0.052960	0.011244	0.078280	33.847808
Amount	284807.0	8.834962e+01	250.120109	0.000000	5.600000	22.000000	77.165000	25691.160000
Class	284807.0	1.727486e-03	0.041527	0.000000	0.000000	0.000000	0.000000	1.000000

	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	...	V20	V21	V22	V23	V24	V25	V26	V27	V28	Class
0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	0.090794	...	0.251412	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	0
1	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	-0.166974	...	-0.069083	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	0
2	-1.358354	-1.340163	1.773209	0.379780	-0.503198	1.800499	0.791461	0.247676	-1.514654	0.207643	...	0.524980	0.247998	0.771679	0.909412	-0.689281	-0.327642	-0.139097	-0.055353	-0.059752	0
3	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	-0.054952	...	-0.208038	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	0
4	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	0.753074	...	0.408542	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
284802	-11.881118	10.071785	-9.834783	-2.066656	-5.364473	-2.606837	-4.918215	7.305334	1.914428	4.356170	...	1.475829	0.213454	0.111864	1.014480	-0.509348	1.436807	0.250034	0.943651	0.823731	0
284803	-0.732789	-0.055080	2.035030	-0.738589	0.868229	1.058415	0.024330	0.294869	0.584800	-0.975926	...	0.059616	0.214205	0.924384	0.012463	-1.016226	-0.606624	-0.395255	0.068472	-0.053527	0
284804	1.919565	-0.301254	-3.249640	-0.557828	2.630515	3.031260	-0.296827	0.708417	0.432454	-0.484782	...	0.001396	0.232045	0.578229	-0.037501	0.640134	0.265745	-0.087371	0.004455	-0.026561	0
284805	-0.240440	0.530483	0.702510	0.689799	-0.377961	0.623708	-0.686180	0.679145	0.392087	-0.399126	...	0.127434	0.265245	0.800049	-0.163298	0.123205	-0.569159	0.546668	0.108821	0.104533	0
284806	-0.533413	-0.189733	0.703337	-0.506271	-0.012546	-0.649617	1.577006	-0.414650	0.486180	-0.915427	...	0.382948	0.261057	0.643078	0.376777	0.008797	-0.473649	-0.818267	-0.002415	0.013649	0

	V9	V7	V4	V3	V18	V17	V16	V14	V12	V11	V10	Class
0	0.363787	0.239599	1.378155	2.536347	0.025791	0.207971	-0.470401	-0.311169	-0.617801	-0.551600	0.090794	0
1	-0.255425	-0.078803	0.448154	0.166480	-0.183361	-0.114805	0.463917	-0.143772	1.065235	1.612727	-0.166974	0
2	-1.514654	0.791461	0.379780	1.773209	-0.121359	1.109969	-2.890083	-0.165946	0.066084	0.624501	0.207643	0
3	-1.387024	0.237609	-0.863291	1.792993	1.965775	-0.684093	-1.059647	-0.287924	0.178228	-0.226487	-0.054952	0
4	0.817739	0.592941	0.403034	1.548718	-0.038195	-0.237033	-0.451449	-1.119670	0.538196	-0.822843	0.753074	0
...	...	...	...	...	...	...	...	...	...	...	...	...
284802	1.914428	-4.918215	-2.066656	-9.834783	0.510632	1.991691	1.107641	4.626942	2.711941	-1.593105	4.356170	0
284803	0.584800	0.024330	-0.738589	2.035030	-1.221179	-0.025693	-0.711757	-0.675143	0.915802	-0.150189	-0.975926	0
284804	0.432454	-0.296827	-0.557828	-3.249640	0.395652	0.313502	0.140716	-0.510602	0.063119	0.411614	-0.484782	0
284805	0.392087	-0.686180	0.689799	0.702510	1.113981	0.509928	-0.608577	0.449624	-0.962886	-1.933849	-0.399126	0
284806	0.486180	1.577006	-0.506271	0.703337	0.167430	-0.660377	-0.302620	-0.084316	-0.031513	-1.040458	-0.915427	0

StarAgile ML Project - Level 2 - Assignment 5 (Credit card Fraud)¶

Assignment Submitted by VINAY M on 15-09-2023¶

	V3	V4	V7	V9	V10	V11	V12	V14	V16	V17	V18	Class
204672	-0.645555	-0.226333	0.657229	-0.160139	-1.012902	-1.419184	-0.447227	-0.839602	0.199874	0.132574	0.985385	0
11195	-3.300996	1.328576	-1.317298	0.680243	0.220483	1.043241	-1.319899	4.194326	0.549076	1.509883	0.959809	0
124222	0.924527	0.379391	-0.666701	0.247487	-0.027557	1.754849	0.891907	0.328812	0.224365	-0.199789	-0.450765	0
201850	-0.967302	-0.689078	0.632001	0.527024	0.424436	-0.035527	0.669456	0.117818	-0.245921	-0.733402	0.182042	0
22203	0.454911	0.419529	-0.268450	-0.056560	0.045373	1.748059	0.647551	0.712818	-0.039861	-0.047598	-0.817727	0
...	...	...	...	...	...	...	...	...	...	...	...	...
176806	-0.855592	0.228456	-0.358298	1.000985	0.060851	-1.024946	-0.120508	0.169038	0.229355	-0.405941	-0.246612	0
275707	0.136099	-0.671405	0.999546	0.113096	-0.107851	-0.984275	0.261438	-0.027189	-0.085751	-0.488393	-0.753509	0
228516	-1.843302	-1.589636	-0.436569	-1.683131	1.587385	-1.152898	-0.543392	-0.279145	-1.160425	0.688561	-0.508723	0
181988	-0.350708	0.291507	-0.857244	0.985568	0.132625	0.318657	1.127814	-0.250795	1.014570	-1.199349	0.731150	0
128213	1.255698	-0.577482	0.357458	-1.054652	0.551640	-0.050280	-1.134693	-1.924152	1.056636	0.994269	-0.541431	0

	V3	V4	V7	V9	V10	V11	V12	V14	V16	V17	V18	Class
541	-1.609851	3.997906	-2.537387	-2.770089	-2.772272	3.202033	-2.899907	-4.289254	-1.140747	-2.830056	-0.016822	1
623	1.088463	2.288644	0.325574	-0.270953	-0.838587	-0.414575	-0.503141	-1.692029	0.666780	0.599717	1.725321	1
4920	-0.359745	2.330243	0.562320	-0.238253	-1.525412	2.032912	-6.560124	-1.470102	-2.282194	-4.781831	-2.615665	1
6108	-2.592844	2.679787	-3.496197	-0.247768	-4.801637	4.895844	-10.912819	-6.771097	-7.358083	-12.598419	-5.131549	1
6329	-4.304597	4.732795	1.713445	-1.282858	-2.447469	2.101344	-4.609628	-6.079337	2.581851	6.739384	3.042493	1
...	...	...	...	...	...	...	...	...	...	...	...	...
279863	-4.518331	1.749293	-0.882850	-2.064945	-5.587794	2.115795	-5.417424	-6.665177	-2.897825	-4.570529	-1.315147	1
280143	-5.004247	1.411850	-1.413170	-1.127396	-3.232153	2.858466	-3.096915	-5.210141	-2.155297	-3.267116	-0.688505	1
280149	-2.213700	0.468308	-2.234739	-0.652250	-3.463891	1.794969	-2.775022	-4.057162	-1.603015	-5.035326	-0.507000	1
281144	-5.399730	1.817092	-2.208002	-1.632333	-5.245984	1.933520	-5.030465	-6.416628	-2.549498	-4.614717	-1.478138	1
281674	-2.583441	0.408670	0.223050	0.577829	-0.888722	0.491140	0.728903	-1.948883	0.519436	0.903562	1.197315	1

	V3	V4	V7	V9	V10	V11	V12	V14	V16	V17	V18
0	0.392344	-0.683097	0.561591	0.422923	0.332750	-1.136593	0.516065	0.482314	0.586791	0.511297	0.832245
1	-0.068806	-0.186772	0.185199	0.802305	0.628323	-0.212722	0.313993	1.614935	0.693126	0.755066	0.821048
2	0.665009	-0.489751	0.309219	0.606942	0.568882	0.054265	0.826149	0.745204	0.594248	0.452472	0.203483
3	0.336469	-0.830804	0.556782	0.733136	0.677200	-0.617462	0.774639	0.697731	0.451042	0.358029	0.480532
4	0.583454	-0.476939	0.385135	0.469683	0.586359	0.051717	0.769567	0.831604	0.513789	0.479409	0.042822
...	...	...	...	...	...	...	...	...	...	...	...
1083	-0.280212	-0.052481	0.268015	-0.436982	-0.763597	0.189688	-0.634812	-0.828426	-0.356488	-0.321102	-0.174954
1084	-0.364597	-0.160192	0.166924	-0.013735	-0.199081	0.468328	-0.097485	-0.501046	-0.130381	-0.090411	0.099397
1085	0.120016	-0.461368	0.010313	0.200765	-0.254616	0.069317	-0.022949	-0.241629	0.037794	-0.403366	0.178862
1086	-0.433278	-0.030839	0.015410	-0.241684	-0.681684	0.121300	-0.545210	-0.772503	-0.250419	-0.328922	-0.246313
1087	0.055806	-0.480405	0.478826	0.756072	0.362509	-0.419863	0.788405	0.232728	0.684100	0.647754	0.925030

	V3	V4	V7	V9	V10	V11	V12	V14	V16	V17	V18
1034	-0.028938	0.215244	-0.057107	-0.258766	-0.265408	-0.064117	0.165791	-0.448842	0.222186	0.343186	0.535590
249	0.472051	-0.875147	0.407203	-0.197510	0.764255	0.079957	0.752472	0.712676	0.706881	0.556909	-0.321766
620	-0.579322	1.520030	0.125260	-0.766961	-0.907406	1.521079	-1.662239	-1.734270	-0.095873	0.288002	0.451817
837	0.158149	0.269037	0.205835	-0.158119	-0.371349	0.356610	-0.170441	-0.393639	-0.480808	-0.261012	0.072120
71	0.407056	0.587768	0.448860	0.010071	0.970674	-0.445291	0.694707	0.765046	0.795928	0.319144	0.527467
...	...	...	...	...	...	...	...	...	...	...	...
330	0.559149	-0.391554	0.459751	0.473459	0.503979	-0.596483	0.739550	0.582491	0.664473	0.481141	0.241855
466	0.142569	-1.150686	0.361839	-0.278550	0.947521	-1.160199	0.509047	0.587001	0.194862	0.583688	0.192458
121	0.528525	0.568243	0.324086	0.249926	0.946708	-0.594036	0.720716	0.650964	0.961131	0.263080	0.675428
1044	0.140302	-0.368338	0.528290	0.966974	0.222017	-0.599992	0.731469	0.146349	0.666369	0.747662	0.791231
860	-0.721413	0.756704	-0.504908	-1.218793	-1.060203	0.618827	-0.988274	-0.891837	-1.351933	-1.381943	-1.354873

	V3	V4	V7	V9	V10	V11	V12	V14	V16	V17	V18
355	0.861609	-1.647351	0.350230	0.867231	0.188575	-0.666022	0.668100	0.588479	-0.425726	0.728907	0.058207
56	0.143976	-1.135981	0.644187	0.456527	0.559347	-0.664365	0.566824	0.766906	0.542486	0.347056	0.315915
478	0.756488	-0.639421	0.583305	0.366950	0.620707	0.045567	0.721023	0.572288	0.656690	0.441218	0.524512
701	0.217533	0.606425	0.016493	-0.422964	-0.079739	0.284474	-0.227130	-0.396259	-0.229108	-0.384921	-0.714449
724	0.021857	0.621158	-0.363425	-0.800482	-0.692378	0.830496	-1.115256	-1.247484	-1.356209	-1.228757	-1.708644
...	...	...	...	...	...	...	...	...	...	...	...
877	-0.393015	0.408555	-0.792380	-0.955737	-1.520436	1.427900	-1.270826	-1.622296	-1.715407	-2.108382	-1.734425
585	0.660294	-0.560941	0.407638	0.606092	0.660264	-0.387922	0.748144	0.700796	0.726260	0.345475	0.576483
305	0.434608	-0.499773	0.306519	1.017902	0.567791	-0.318711	0.910581	0.661180	0.612064	0.351555	0.707328
716	-0.722468	1.273285	-0.890183	-2.006451	-1.222352	0.775321	-1.150153	-1.196180	-1.542543	-1.649477	-1.736641
631	-0.692331	1.558674	-0.028177	-1.026876	-1.056686	2.254831	-1.761992	-1.926035	-0.292985	-0.079308	0.438713